Parallel Accelerated Matting Method Based on Local Learning

To pursue effective and fast matting method is of great importance in digital image editing. This paper proposes a scheme to accelerate learning based digital matting and implement it on modern GPU in parallel, which involves learning stage and solving stage. Firstly, we present GPU-based method to accelerate the pixel-wise learning stage. Then, trimap skeleton based algorithm is proposed to divide the image into blocks and process blocks in parallel to speed up the solving stage. Experimental results demonstrated that the proposed scheme achieves a maximal 12+ speedup over previous serial methods without degrading segmentation precision.

[1]  George Chen,et al.  Parallel active contour with Lattice Boltzmann scheme on modern GPU , 2012, 2012 19th IEEE International Conference on Image Processing.

[2]  Jian Sun,et al.  Fast matting using large kernel matting Laplacian matrices , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Qingsong Zhu,et al.  Learning based alpha matting using support vector regression , 2012, ICIP.

[4]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[5]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[6]  Jianfei Cai,et al.  Real-Time and Temporal-Coherent Foreground Extraction With Commodity RGBD Camera , 2015, IEEE Journal of Selected Topics in Signal Processing.

[7]  Yuanjie Zheng,et al.  Learning based digital matting , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Kwan-Liu Ma,et al.  Fast Closed-Form Matting Using a Hierarchical Data Structure , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Jue Wang,et al.  A perceptually motivated online benchmark for image matting , 2009, CVPR.

[10]  G. Lu,et al.  GrowMatting: A GPU-based real-time interactive method for image matting , 2010, 2010 25th International Conference of Image and Vision Computing New Zealand.

[11]  Qing Zhang,et al.  Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Zicheng Guo,et al.  Parallel thinning with two-subiteration algorithms , 1989, Commun. ACM.